At sige det her er skræmmende læsning er en underdrivelse.

Meget af det @TyraGroveKrause @SSI_dk @SSTSundhed siger er simpelthen forkert, så lad os tage en kigger på det der bliver sagt og hvordan Danmark, igen, har givet op mht. coronaen.

🧵👇

https://twitter.com/igumrasmussen/status/1601497812059160576?s=61&t=bD1CQaMJmzSJKBHvLdE_8w

Lad os starte med titlen på artiklen - det er typisk @IgumRasmussen, som jo også har haft artikler med titler som "skidt pyt" og en helt masse anti-vaccine sludder.

Det er skuffende. Og problemet er jo at når "flere smittes" så dør der også flere.

Men vær ligeglad, ikke?

Hvad er den ny strategi så? Tjooo, lidt som den gamle strategi - altså, der er ikke nogen.

Det kan åbenbart ikke lade sig gøre at dæmpe smitten, så nu må folk jo bare smittes 🤷‍♂️ .

Hvad Krause glemmer, er at problemet ikke bare er at vi får "alvorlig sygdom", vi får også død.

Det vigtigste for mig har altid været, at oplysningen skal være rigtig - og når man nu vælger masse-smitning, så bliver @SSI_dk også nødt til at fortælle befolkningen, at denne 'strategi' kommer til at lede til mange døde - det må man så acceptere. Eller ej.

Problemet er jo, at vi ikke kan løse alt mht. til coronaen kun ved brug af vacciner. Hvad med dem der ikke har noget immunforsvar? Hvad med dem med svagt immunforsvar - f.eks. kræft patienter, transplantions patienter, sklerose, gigt, osv?

Skidt pyt med dem?

Hvad med dem der bor sammen med forældre, børn, ægtefæller, osv. der har dårligt immuneforsvar? Skal de bare smutte på arbejde, vel-vidende at kolleger nu ikke skal selv-isolere og heller ikke kan blive testet? Eller vaccineret?

Du skal bare være ligeglad. Det er planen.

Modsat hvad Krause siger, så virker vaccinerne SELVFØLGELIG når det kommer til at dæmpe smitten - kan man stoppe den helt med vacciner? Nej, det kan man ikke, men de dæmper.

Men det ved @SSI_dk åbenbart ikke - som jo også har besluttet at dem under 50 ikke skal vaccineres.

Man kan jo også gøre meget andet end at vaccinere - f.eks. kunne man oplyse befolkningen ordentligt om risiko, inkl. luftbåren smitte. Det gør man openbart ikke i Danmark, fordi det er jo nemmere bare at lukke øjnene 🤷‍♂️.

https://twitter.com/trishgreenhalgh/status/1479906806038249478?s=61&t=bD1CQaMJmzSJKBHvLdE_8w

Man kunne jo f.eks. overveje....

https://twitter.com/trishgreenhalgh/status/1479913037964398599?s=61&t=bD1CQaMJmzSJKBHvLdE_8w

Og istedet for at sige at der altså ikke er mulighed for almindelige borgere at finde ud af om man har COVID-19 eller influenza, så kunne man jo anbefale hjemme test - dem har vi haft i 2.5 år nu. Det ved i godt, ikke @SSI_dk @SSTSundhed?

Og lad os lige huske igen, at COVID-19 er en langt mere farlig sygdom end influenza - og, nej, den udvikler sig ikke ligesom influenza, den udvikler sig langt hurtigere.

Og jo flere smittede vi har, jo hurtigere går det jo. Skidt pyt, ikke?

Og ja - så den helt store - senfølger. Jo jo, de er helt sikkert med i vores kalkyle, men nej, de er overhovedet ikke med i vores kalkyle 🤷‍♂️.

Ja, vacciner hjælper helt sikkert mht. senfølger, men desværre ikke godt nok - den eneste måde at undgå dem, er at undgå smitte...

... så lad os give befolkningen de værktøj de behøver, hvilket starter med at give dem god information - ikke det samme "der er altså ikke noget vi kan gøre, og den er iøvrigt mild". Sludder.

Men @SSI_dk kan desværre ikke se noget alternativt end, at lade viruset køre lås, og så ellers håbe for det bedste for de borgere der er over 50 med godt immunforsvar, imens man lader dem under 50 og dem med immun sygdomme i stikken.

For alternativet er nedlukning?

Sikke dog noget sludder - der er INGEN seriøse infektionssygdoms eksperter der foreslår nedlukninger. Ingen.

Men, man kunne jo starte med at følge internationale retningslinjer fra bl.a. @WHO. Men det gør Danmark - igen - ikke. Og baseret på hovedløs oplysning.

Og ang. "alternativet", så er der altså mange, inklusive at gøre vaccination muligt for alle, bruge mundbind når det giver mening, muliggøre hjemme test, udlufte bygninger, oplyse borgerene ordentlig, osv.

@TyraGroveKrause @SSI_dk @SSTSundhed start her:
https://www.nature.com/articles/s41586-022-05398-2

Like it or not (I don't..), but mass-scale clinical testing for COVID-19 is likely going away in the near future.

Just as we're entering a new wave 📈.

A 🧵 on priority areas I think we *must* focus on:

1️⃣ Wastewater surveillance
2️⃣ Clinical sequencing
3️⃣ Home testing

Important background article from @nature on some of these issues:

https://www.nature.com/articles/d41586-022-00788-y


Until now, we have been relying on case and hospitalization data to get a sense of the waning and waxing of the COVID-19 pandemic. This relies on (a) testing being done, and (b) reported.

However, this is likely going away. E.g., 🇩🇰 👇 (NOT 🇩🇰 specific!)

https://twitter.com/K_G_Andersen/status/1504198260281339905?s=20&t=XIq6AL00xr0UUmBjhMkZOQ

Since testing will get more biased and reporting may likely (mostly) disappear, we're left in the blind on the rise of future waves - until they hit hospitals 🙈 🙉.

Now, we should solve this by *not* decreasing testing, but that's unlikely to happen.

Enter,

1️⃣ Wastewater surveillance

To make up for the lack in testing, we need to heavily invest in wastewater surveillance - measure the amount of SARS-CoV-2 in communal wastewater - across the country. The WHOLE country.

Test and report daily. "Daily" means "every day" btw...

Via our SEARCH Alliance here in San Diego, we're already doing this. But these programs need sustainable funding - which isn't currently the case. We practically have to beg for money on a daily basis - and we're not always successful, leading to delays.

https://searchcovid.info/dashboards/wastewater-surveillance/

Importantly, this surveillance *must* be combined with sequencing of the wastewater - combined with tools such as "Freyja" that @josh__levy developed in our lab.

This allows us to monitor the dynamics of SARS-CoV-2 variants in wastewater over time. E.g., 👇 

Note, the frequency and speed at which we see lineage displacement = 🤯. We have never observed this with other viruses - so it's key we keep a close eye on SARS-CoV-2 dynamics. This is *not* a process that will magically stop.

Paper on how we do 👆 
https://www.medrxiv.org/content/10.1101/2021.12.21.21268143v1

Also, here's a link to Freyja (available on Docker, conda, Github, etc).

https://andersen-lab.com/secrets/code/#freyja

2️⃣ Clinical sequencing

Widescale testing will decrease significantly, which will greatly limit our ability to do non-wastewater based "genomic surveillance" of SARS-CoV-2. 

E.g., what SEARCH is currently doing:

https://searchcovid.info/dashboards/sequencing-statistics/

However, testing of hospitalized COVID-19 patients will presumable continue and it's important to sequence those - and do so consistently. 

Why? So we can (a) compare to our wastewater-based sequencing, and (b) if deviations are seen, understand reasons why.

Why might we see deviations? E.g., age-skews in cases, potential differences in variant virulence, etc.

As above, these programs need sustainable support. Do we really need to beg for money for these programs?

SEARCH has been doing genomic surveillance since February, 2020.

3️⃣ Home testing

Finally, we really need to get fully behind testing at home. It simply doesn't make any sense to either (a) go queue forever to get a test - with a result reported days later, or (b) not get a test because they're no longer offered.

This model NEVER made sense.

Because "One size does not fit all" and rapid (antigen) testing is one of the most undervalued tools in our COVID-19 toolbox, @michaelmina_lab and I wrote about it in 2020:

https://www.science.org/doi/10.1126/science.abe9187

These tests need to be:

1️⃣ Widely available
2️⃣ Cheap, for those who can afford them and free for those who can't
3️⃣ Ideally, (anonymous) reporting of test results should be included via an app
4️⃣ Positive results should be combined with accessible/affordable treatment options

SARS-CoV-2 will not magically go away because we wish it so - it's "endemic delusion".

Things will get better, but only if we make it so. Wishing away a virus will achieve the opposite - we have more than two years of data and an estimated ~20m dead to underscore that point.

Finally, I want to thank CDC, NIH, and Conrad Prebys foundation for having supported our work so far.

I'm glad to see the CDC is planning on establishing "Pathogen Genomics Centers of Excellence", which I hope can lead to more sustainable 🇺🇸 programs.

https://www.cdc.gov/amd/whats-new/pgcoe-nofo.html

You can read the unrolled version of this thread here: https://typefully.com/K_G_Andersen/8r3HKeN

Future of testing

We recently published two preprints that provide the strongest evidence yet that the COVID-19 pandemic began at a market selling live animals in Wuhan.

An almost mirror-image of SARS just two decades earlier.

https://zenodo.org/record/6291628
https://zenodo.org/record/6299600

Long 🧵 👇

Why do we believe the Huanan market was the origin?

1️⃣ Precedent
2️⃣ Clustering of hospitalizations, cases, deaths
3️⃣ Not a superspreading event
4️⃣ Two introductions
5️⃣ Susceptible animals sold
6️⃣ Clustering where animals were sold
7️⃣ Animal-associated objects ➕

Details 👇

Before I dive in, I want to answer the most important question - does this mean we have solved the origin of the pandemic?

No, we have not. It is clear that it began at a market, but most everything upstream remains a black box and there's still a ton to do.

More at the end 🧵.

📖 In the first preprint, led by @jepekar we show that:

"SARS-CoV-2 emergence very likely resulted from at least two zoonotic events"

https://zenodo.org/record/6291628

📖 In the second preprint, led by @MichaelWorobey we show that:

"The Huanan market was the epicenter of SARS-CoV-2 emergence"

https://zenodo.org/record/6299600

1️⃣ Precedent

The emergence of SARS-CoV-2 looks a lot like that of SARS-CoV-1, especially:

✅ Virus similarity (both are sarbecoviruses)
✅ Timing (November 2002 vs November 2019)
✅ Host range (broad)
✅ Association with wet markets

For example, compare...

The emergence of a novel coronavirus isn't exactly news - it happens frequently. Here's a sampling of the ones we know about 👇 .

And, of course, since the beginning of the pandemic, many closely related viruses have been found, including BANAL-52:

https://www.nature.com/articles/s41586-022-04532-4

These viruses are widespread all across South-East Asia because their reservoirs - horseshoe bats - are widely distributed, including, yes, in Hubei province. Importantly, virus diversity is massively undersampled.

Excellent paper led by @SpyrosLytras:

https://www.science.org/doi/10.1126/science.abh0117

If we go back to SARS1, we know that infected animals were found on farms in Hubei in both 2003 and 2004 - outside Wuhan. In fact, more SARS1 infected animals were found on Hubei farms than anywhere else in China - and Hubei supplied animals to Guangdong.

https://twitter.com/K_G_Andersen/status/1488918972003094529?s=20&t=Lc1K_fAPVgT9VdVW8W-aIw

Interestingly, the SARS1 genomes from Hubei - unlikely those from Guangdong - are significantly closer to the SARS1 genomes from human patients.

Were Hubei farms the potential source for SARS1 and SARS2? Maybe, but need much more research.

References:
https://twitter.com/K_G_Andersen/status/1488718228238974983?s=20&t=j-VcqV9tc-t9wMEEpVHFeg

Because SARS-CoV-2 became a pandemic virus and SARS-CoV-1 did not, we would expect to find some 'key' features in this virus. We discussed this in our "Proximal Origin" paper:

https://www.nature.com/articles/s41591-020-0820-9

And this review:

https://www.cell.com/cell/fulltext/S0092-8674(21)00991-0

I previously did a lengthy thread on one of these features - the furin cleavage site. Some have suggested that the structure of this site is suggestive of engineering - that couldn't be further from the truth (quite the opposite).

https://twitter.com/K_G_Andersen/status/1391507230848032772

So why Wuhan? Another thread from @MichaelWorobey:

https://twitter.com/MichaelWorobey/status/1438202006678630404

2️⃣ Clustering of hospitalizations, cases, deaths around the Huanan market

@MichaelWorobey wrote an excellent perspective on the clustering of hospitalizations early in the pandemic that showed a *very* clear association to the Huanan market:

https://www.science.org/doi/10.1126/science.abm4454

I previously talked about the clustering of cases and excess deaths when I did a (rather lengthy...) rundown of the @WHO mission report:

https://twitter.com/K_G_Andersen/status/1376954932004196352

In the first part of Worobey et al., we go much deeper on the "clustering" analysis:

✅ Early cases are clearly associated with the market
✅ Association is non-random and not a result of age/demographics
✅ The Huanan market was the early epicenter

https://zenodo.org/record/6299600

We extracted early case locations from the WHO mission report, later (probable) case locations from Weibo, and created null models based on population density or Jan/Feb cases.

No matter how we look at the data, the association is clear and non-random.

Further, the Huanan market is *the only* place in Wuhan where early cases had a clear association - there are no other epidemiological links to any other place in the city.

All of that changed as the outbreak spread - then we see clear association based on demographics.

3️⃣ Not a superspreading event

Some have taken this to mean that the market was "merely a superspreading event". This is very unlikely:

✅ Clustering inside market
✅ Timing + multiple spillovers

Further, the doubling rate in the market was ~3-4 days (estimated from WHO data👇)

4️⃣ Two introductions

Let's hop over to Pekar et al., where we show that it's very likely that SARS-CoV-2 jumped multiple times (like SARS-CoV-1) - with one  'successful' lineage ("B") likely spilling over in late November and lineage "A" 1-2 weeks later.

One quick comment on "multiple spillovers" as some people find this exceedingly unlikely ("one spillover is unlikely, so two spillovers are much more unlikely").

This is not correct. We're dealing with conditional probability - one happens, two (and more) are likely to happen.

As Joel Wertheim put it - "we failed to climb Mount Everest for hundreds of thousands of years. And then, in just one day, two people did".

This is the same. The "(un)likelihoods" are upstream of infected animals - with just the right virus - ending up in a market. Once done,💥

We have known for a while that two early lineages of SARS-CoV-2 existed - Lineage "A" and Lineage "B", using the PANGO naming convention.

These two differ at two sites - 8272 ("A"=T; "B"=C) and 28144 ("A"=C; "B"=T) so "A" is T/C and "B" is C/T.

https://zenodo.org/record/6291628

Genomic data show that "intermediates" (C/C; T/T) between A and B may have existed during the early outbreak in Wuhan - suggesting that e.g., A evolved to B or B evolved to A in humans.

However, we show that this likely wasn't the case - early intermediates are due to errors.

Lineage A is closer to closely related viruses like RaTG13 and BANAL-52, however, we can't simply say that this means that A is the ancestor of B as some have done previously. 

E.g.,

https://academic.oup.com/mbe/article/38/12/5211/6353034

Trying to establish a rooted phylogeny of SARS-CoV-2 requires much more careful analysis than simply performing "outgroup" rooting (like ☝️). 

In Pekar et al., we performed several Bayesian analyses to estimate likely roots and also created a putative "common ancestor".

Once we obtain proper (posterior) estimates of the early SARS-CoV-2 phylogeny - and incorporate factors such as sampling times and evolutionary models - we can simulate plausible scenarios of early virus diversity and compare to the diversity that was actually observed.

These analyses make it clear that it is *much* more likely that A and B resulted from independent spillovers - one did not evolve into the other in the human population.

Further, our timing estimates show that B likely spilled over late-November and A a little after that.

These findings need to be seen in the context of a clear geographical association of *both* Lineages A and B with the Huanan market.

Further, as we were wrapping up our preprints, Gao et al. posted a preprint showing that Lineage A was indeed present at the Huanan market - in an environmental sample ("A20"), no less.

https://www.researchsquare.com/article/rs-1370392/v1

We also don't see any evidence of intermediate genomes at the Huanan market in the Gao et al. preprint. We can therefore conclude:

✅ Both Lineage A and B found at Huanan market
✅ Early intermediates likely not real
✅ Very likely two independent spillovers at the Huanan market

5️⃣ Susceptible animals sold at the Huanan market in Nov/Dec 2019

Having established a clear - undeniable - association of early cases to the Huanan market of early cases, we next investigated likely conduits.

We already knew live animals were sold 👇 

https://www.nature.com/articles/s41598-021-91470-2

But what was on sale during the critical months of November and December? Well, same types of animals described in the Xiao et al. preprint, including raccoon dogs.

Based on surveys co-author Chris Newman was part of, we had data for November:

And based on publicly available images and recordings, we know e.g., raccoon dogs were also on sale in December.

https://www.youtube.com/watch?v=Je0_U2ym_r0

6️⃣ Clustering of environmental samples inside the Huanan market where animals were sold.

So far we have shown:

✅ Clear association of cases to the Huanan market
✅ Multiple spillovers likely happened at the Huanan market
✅ Susceptible animals were sold at the Huanan market

But what about inside the market? It's previously been reported that early cases primarily came from the western side of the market (where animals were sold), but anything more specific than that?

Well, yes. Enter China CDC report from January, 2020

Make sure you read it - it's short and has a lot of key details, including location information of the 585 environmental samples they took from the Huanan market.

Their conclusion?

"... it's highly suspected that the current epidemic is related to the trade of wild animals".

The report contains details about the sampling from inside the market. We used this information to reconstruct a detailed map of the Huanan market, overlaying information about live animal sales, environmental sample positivity, and human cases.

Again, make sure you read the report - and the many other China/Wuhan CDC reports, official news articles, and other relevant material we had translated for our manuscript.

Available here ("File S1"):

https://zenodo.org/record/6291868

What did we find?

✅ Very clear clustering inside the market
✅ Clustering specifically where live animals were sold

So not only do we find clustering *outside* the market - we find very tight clustering *inside* the market.

Where live animals were sold.

⚠️ Some caveats to these analyses.

Because we didn't perform the sampling inside the market ourselves, we don't have access to all information - most importantly, sampling scheme.

Some specifics on potential bias from @acritschristoph:
https://twitter.com/acritschristoph/status/1497946228272910337?s=20&t=tXd0UmNZxQN_-mO4qWNOEg

⚠️ The Gao et al. preprint reported that a host read analysis of key raw sequencing data - which we encourage our colleagues in China to make publicly available - showed no animal association.

Some comments from @arambaut on why those analyses are flawed:
https://twitter.com/arambaut/status/1497696812550918144

7️⃣ Environmental samples clearly associated with animal sales

From the China CDC report we also know what objects positive environmental samples came from.

Most environmental samples (5) came from a single shop that we know sold live animals. What objects were positive?

Well...

✅ Cage
✅ Two carts
✅ Feather remover
✅ Ground

From the Gao et. al preprint we also know:

✅ Sewage outside shop

Here's the crazy thing - co-senior author @edwardcholmes visited the Huanan market in 2014 and took photos of this exact shop! Including raccoon dogs 🤯. 

Photos:

Okay, let's have a closer look at these:

✅ Cage
✅ Two carts

Well, cage is clearly there. What about the carts (often used to transport mobile cages and other goods)?

Well, 🔍 .

Two carts in Eddie's photo.

What about:

✅ Feather remover

Well, 🔍 .

The raccoon dogs in Eddie's photo sat on top of birds.

What about:

✅ Sewage outside shop

Well, 🔍 .

The raccoon dogs in mobile cages in Eddie's photo sat on top of the sewage system.

So when we see that the following environmental samples are positive from the China CDC survey:

✅ Cage
✅ Two carts
✅ Feather remover
✅ Ground
✅ Sewage

All those are seen in Eddie's photo - clearly associated with live animal sales.

🤯

And that Lineage A sequence reported in the Gao et al. preprint?

It was located here (figure from @alchemytoday):

8️⃣ Summary

Combined, all of these data clearly show that the Huanan market was the epicenter of SARS-CoV-2 emergence - with a very likely origin via infected animals.

Other non-market hypotheses are possible, but I don't find any of them plausible. Because, science.

9️⃣ Next steps

So have we solved the origin of the COVID-19 pandemic? No. The data very clearly point to a market beginning via infected animals, however, we have limited understanding of upstream events.

❓What animals?
❓Farmed or wild?
❓Connectedness?
❓Epi?
❓Future risk?

Answering all of these questions will require significant further work and is critical. Tracing back supply chains; genomic epi; serology studies at farms and more widely; in-depth epi/serology studies in Wuhan with a focus on Nov / Dec; ruling out alternative hypotheses, etc.

I'm hopeful that @WHO SAGO - w. China CDC - will help bring further clarity to these very important questions. We need to more fully understand how pandemics start.

To quote @mvankerkhove: Science, solutions, solidarity. 

I'll add, collaboration, collaboration, collaboration.

1️⃣ 0️⃣ Extra material

Several excellent resources to explore these studies and questions more.

For example, coverage from:

@nytimes 
https://www.nytimes.com/interactive/2022/02/26/science/covid-virus-wuhan-origins.html

@washingtonpost
https://www.washingtonpost.com/health/2022/03/01/nih-review-lab-safety/

@ScienceMagazine 
https://www.science.org/content/article/do-three-new-studies-add-proof-covid-19-s-origin-wuhan-animal-market

@Nature 
https://www.nature.com/articles/d41586-022-00584-8

⭐ threads from co-authors and Tweeps:

@MichaelWorobey:
https://twitter.com/MichaelWorobey/status/1497607313397481472

@angie_rasmussen:
https://twitter.com/angie_rasmussen/status/1497692666997329923?s=20&t=l6TLZfiIt2eLazH_b54owA

@whstancil:
https://twitter.com/whstancil/status/1497971502720901120

@emmecola:
https://twitter.com/emmecola/status/1498633737411567619?s=20&t=piLqpOQNo2tw30QZje6ylg

@stuartjdneil:
https://twitter.com/stuartjdneil/status/1498058588253835266?s=20&t=JEFCg4OApygs6HD5RuNk5Q

@PhilippMarkolin:
https://twitter.com/PhilippMarkolin/status/1498283470627479557?s=20&t=jx0Lw4ifiSLGZ35mIb6vHA

Background information on various aspects of the "origin" question:

@mstandaert on animal farming/sales
https://www.washingtonpost.com/world/asia_pacific/china-covid-bats-caves-hubei/2021/10/10/082eb8b6-1c32-11ec-bea8-308ea134594f_story.html

Article on Danielle Anderson who worked at the WIV just as the pandemic started
https://www.bloomberg.com/news/features/2021-06-27/did-covid-come-from-a-lab-scientist-at-wuhan-institute-speaks-out

My Q&A with @nytimes
https://www.nytimes.com/2021/06/14/science/covid-lab-leak-fauci-kristian-andersen.html

And finally, these two articles:

@janeqiuchina's must-read on Shi Zhengli
https://www.technologyreview.com/2022/02/09/1044985/shi-zhengli-covid-lab-leak-wuhan

@stuartjdneil, @past_is_future, and @STWorg on conspiracy theories surrounding the "origin" question:

Finally, if you want to take a look inside the Huanan market @babarlelephant created this 🤯 resource:

http://babarlelephant.free-hoster.net/visiting-the-wuhan-seafood-market/

You can read the unrolled version of this thread here: https://typefully.com/K_G_Andersen/718agsO

Huanan market origin

The most recent 🇩🇰 COVID-19 report from @SSI_dk was released earlier today 👇.

Let's take a look at some of the key numbers/graphs and provide some context for why I find the situation (and strategy) concerning 🧵.

https://files.ssi.dk/covid19/tendensrapport/rapport/ugentlige-tendenser-covid19-andre-luftvejs-uge7-2022-5l9s

1/ 

First, incidence. As has already been discussed, the number of cases have increased steadily since the beginning of the year.

Note also the very high positivity rate as well - close to 40% (PCR) for week 6. Cases are not simply found "because 🇩🇰 tests a lot".

2/ 

The same trend is reflected in hospitalizations, which are also going up - as already discussed.

ICUs remain stable or are decreasing.

Deaths are going up - a trend also observed in recent days with ~30-45 daily deaths.

3/ 

Here's a look just at deaths.

Green = "with" COVID-19 (increasing)
Magenta = "from" COVID-19 (increasing)

More about these in a bit.

4/ 

Here's another graph with deaths, classifying them into various categories of "from", "with", or "currently unclassified". Note, in recent weeks (as expected) - many still remain unclassified (arrow).

5/ 

Trying to classify hospitalizations or deaths into "with" or "from" is not a new problem (it's come up multiple times since the start of the pandemic).

Nor is it a problem specific to Denmark.

6/

Of course, because of highly effective vaccines (👏), we expect the ratio to shift towards more "with" cases.

Seems simple to correct then, right?

No. It's not.

Recent article on why this isn't simple.

https://www.washingtonpost.com/politics/2022/01/11/difference-between-being-hospital-with-covid-19/ 

7/

For example, if somebody with heart disease dies of a myocardial infarction three weeks after testing positive for COVID-19, what caused the person to die? A cancer patient four weeks after starting chemo? A transplant patient?

These issues compound and also cause delays.

8/

Back to the report. Denmark has very high vaccination and booster rates - which are *unquestionably* keeping cases/hospitalizations/deaths lower than they otherwise would be.

Booster rates remain flat though - and there are plans to cancel/pause the program shortly.

9/ 

Here's where one of the main issues of having 'rampant' cases start to become a big issue - an (entirely predictable) shift in age groups.

We're now seeing that effect in 🇩🇰 - starts in the young, then moves to the elderly. We have seen this in *every* wave.

10/ 

This effect is important when we consider "from" vs "with" COVID-19, because we see that most of the "with" group is driven by younger people and the "from" group by the elderly.

Red = "from"
Blue/Green = "with"

11/ 

If we look at nursing homes we're seeing this effect especially clearly. Again, this is entirely predictable and expected when cases are (very) high. 

More nursing homes having outbreaks.
More cases.
More deaths.

12/ 

As incidence remains high - because very little is being done to lower community transmission with no restrictions in place - we might expect cases to remain high for the foreseeable future, with a steady shift in age groups affected.

Wastewater signal is increasing.

13/ 

So looking at this report - and only focusing on population-based acute effects - I see several trends that concern me.

Are these acute consequences acceptable? For me, no, but politicians need to decide. Are we making decisions based on a realistic outlook? I'm not sure.

14/

Of course, there's a reason why I mention "acute" here - because all of this completely ignores potential long-term consequences of virtually everybody getting infected in a society - likely at least twice a year if no restrictions are in place.

15/

I have termed this "endemic delusion" - I'm worried that we're forgetting lessons over the last two years in a desperate attempt to "get back to normal" - a "normal" that unfortunately doesn't exist at the moment.

https://www.science.org/content/article/it-time-live-covid-19-some-scientists-warn-endemic-delusion

16/

US-centric, but very important writing from @gregggonsalves @nytimes on this topic as well:

https://www.nytimes.com/2022/02/17/opinion/aids-pandemic-covid.html

17/

We're also forgetting issues of "long" COVID-19 - which is significant. And, unfortunately, while there is *no* question that vaccination significantly lowers the risk of "long" COVID-19, it may still pose a very substantial problem.

Recent summary:
https://ukhsa.koha-ptfs.co.uk/cgi-bin/koha/opac-retrieve-file.pl?id=fe4f10cd3cd509fe045ad4f72ae0dfff

18/ 

And we're forgetting The Millions Of People Stuck In Pandemic Limbo @edyong209 🧵👇

https://twitter.com/edyong209/status/1493969707849953284?s=20&t=O5aZElHYawyD3VWgMBIEVw

19/

We're forgetting - or ignoring - that Omicron is milder, but not mild; that immunity wanes and new variants will emerge - but we don't know when, where, or how; and that access &amp; equity REALLY matter.

https://twitter.com/K_G_Andersen/status/1492265606128627712?s=20&t=O5aZElHYawyD3VWgMBIEVw

20/

All of these things matter in 🇩🇰 - my home country and where my family lives - but none of this is specific to 🇩🇰 since almost all (western) countries are moving in the same direction.

I talked about that with @ASlavitt last week @inthebubblepod.

https://twitter.com/ScrippsRTI/status/1493652527081877505?s=20&t=7gYmRkaA1AV11qOXVYo93A

21/

Yes, we have been here before.

https://twitter.com/K_G_Andersen/status/1486747833382305799?s=20&t=i7w8HfgpSntxhWuN41pJ7w

22/

If we really want to ensure that we can get "back to normal" ASAP we need to science the shit out of this and keep innovating like there's no tomorrow.

Having long-term, realistic, data-driven, multi-pronged strategies will get us there. 🙈🙉 will not.

fin/

Denmark COVID-19 situation - update

Given discourse about 🇩🇰 COVID-19, it's necessary to point out that I - and likely most 🌎 ID experts - have no trouble understanding the 🇩🇰 data (which isn't unique).

It's about fundamental disagreements on the interpretation of the data and strategy chosen.

🧵👇 for nuance.

A few things first:

1. Denmark has very high vaccination and booster rates that *greatly* reduce disease.

2. Yes, people "with" and "for" COVID-19 are included in official statistics.

However, there's a big difference between "it's not as bad as it could be" vs "it's over".

We're clearly in a better place than 2020, however, we're far from across the finish line. Still a lot of unknowns and "let it rip" strategies ignore several key aspects.

Please read:
https://twitter.com/K_G_Andersen/status/1492265606128627712?s=20&t=aNrEU3Ml5ZCxmX_XAD8Frw

I had a great discussion with @ASlavitt about much of this last week - we didn't cover all points, but it was a good robust discussion about some of the key ones @inthebubblepod.

https://omny.fm/shows/in-the-bubble/the-country-that-decided-the-pandemic-is-over-with

My main concern - and I believe this is what we're seeing in 🇩🇰, but also in many other countries - is that we're becoming delusional. In many cases, trying to make very specific data fit a narrative we all so desperately want to be true.

Please read:
https://twitter.com/K_G_Andersen/status/1491807696193949696

We have been here before - many times.

https://twitter.com/K_G_Andersen/status/1486747833382305799

Specifically on Denmark, more nuance 👇

Pretending that alternative strategies are an easy pick would be naive. I understand Denmark's choice, but I don't agree.

https://twitter.com/K_G_Andersen/status/1486427000499675142

Not a realistic view:

https://twitter.com/K_G_Andersen/status/1486421600668844032

Whatever the strategy, it has to be realistic and based on sound data-driven decision making.

https://twitter.com/K_G_Andersen/status/1486425025066401792

There is *no* question that highly vaccinated countries like Denmark are in a much better place today than in 2020 (although, equity, people!!). 

However, this brings me back to my first point there's a big difference between "it's not as bad as it could be" vs "it's over".

This will be my final comment on this topic for a while.

Remember, if we really want to ensure that we can get "back to normal" ASAP we need to science the shit out of this and keep innovating like there's no tomorrow.

Denmark COVID-19 situation

As we are now in a "let it rip" strategy, I hope decision makes will remember that:

1. Disease is much milder in people with immunity, but by no means "mild" in many

2. Not all can be protected via vaccines and the virus is no less dangerous to them

3. Access and equity matter

4. Viruses mutate and new variants will emerge - with unknown virulence and phenotypes (although immune evasion is a given)

5. We don't understand the mechanism(s) of variant emergence

6. Immunity wanes - and we don't understand the potential role of antigenic sin/imprinting

7. Viruses don't have ears, so it doesn't matter how many times we tell it "it's over" 🤦.

If we really want to ensure that we can get "back to normal" ASAP we need to science the shit out of this and keep innovating like there's no tomorrow.

I somehow deleted one of the most critical points...

8. While we understand "acute" disease relatively well, we have a poor understanding of "long" COVID - including after post-vaccination infections

To this latter point, the news is rarely reassuring
https://twitter.com/EricTopol/status/1491514278817374210?s=20&t=5J_FcesUKrgIzdwijHEUaQ

Adding this absolutely brilliant commentary by @ArisKatzourakis on what "endemicity" is, and, importantly, isn't.

Make sure you read it - if you haven't already.

https://www.nature.com/articles/d41586-022-00155-x

"Let it rip"

"Endemic Delusion".

Used to describe the delusional belief that the pandemic is over and that we can get back to 2019 life by suppressing the fact that we need to keep innovating and fight the virus.

Best exemplified by Denmark 👇 

Denmark effectively declared the pandemic over ~2 weeks ago, declassified the virus so it's no longer a "critical disease", and ended all restrictions.

This is despite the fact that cases, hospitalizations, and test positivity are higher than ever - with deaths rapidly rising.

Yet, all of this is basically ignored by Danish media by now - mostly via:

1. Focusing on ICU numbers - which are stable, however, ignoring the fact that deaths are going up.

2. Endlessly focusing on "with COVID" vs "for COVID", ignoring the fact that it's all "COVID".

As I have said previously, I disagree with the Danish approach, but choosing a strategy is far from easy. However, whatever the strategy, it *must* be based on realistic expectations and not delusion - sadly, the strategy in Denmark is based on the latter.

The issue is that if we simply declare the pandemic over - when that is very much not the case - we stop innovating and fighting, which will only prolong the pandemic and make it worse.

We need to innovate our way out of this - better vaccines, drugs, masks, tests, and systems!

Endemic Delusion

Some comments on this preprint that made the rounds today.

Is this evidence of a lab-cultured ancestral SARS-CoV-2?

Nope - the data prove it's not, and likely we're just looking at common sample contamination from early clinical sequencing. 

1/🧵

https://www.researchsquare.com/article/rs-1330800/v1

❗️First up - this is not a dig at the paper or the authors - I think it's fine. However, the *conclusions* made based on this paper are way off base - as one might expect.

If you want the files behind my analysis, they're available here: https://console.cloud.google.com/storage/browser/andersen-lab_project_sars2-proximal-origins/2022.02_contaminated-samples?project=andersen-lab-primary

2/

Early during the pandemic we had two SARS-CoV-2 lineages - A and B. These are distinguished by "T" in A and "C" in B at position 8782 and by "C" in A and "T" in B at position 28144.

So "A" becomes T/C and "B" C/T at these positions.

3/

Closely related viruses like BANAL-52 and RaTG13 have T/C at these positions, so A is closer to those than B. Many have concluded that that means that A is the "root" of the SARS-CoV-2 tree and ancestral to B.

That is not a correct assumption (e.g., because of reversions).

4/

So we don't actually know the "root" of the tree - and we don't know if e.g., A is ancestral to B or B is ancestral to A.

I'm not going to go into more details on this as it's technical - we'll have a study on it soon.

5/

The three samples identified in the paper that are contaminated are:

SRR13441704
SRR13441705
SRR13441708

These are samples unrelated to SARS-CoV-2, however, they contain SARS-CoV-2 reads - because the sequencing run was contaminated, as happens often.

6/

This allows us to assemble SARS-CoV-2 genomes from these samples - presumably from early (~December, 2019 / January, 2020) specimens. This is what the authors of the preprint did.

Here's the problem though - the samples are *super* messy.

7/

Here's just one example - at three key positions (8782, 18060, 28144) for sample SRR13441704.

A LOT of mismatches - which means that confidently calling genomes becomes near-impossible. Further, this suggest that we're dealing with mixed samples 

8/ 

Because of this, we can't properly call consensus sequences and do lineage assignment (A vs B) - because of low quality and because of mixed samples.

However, it seems reasonable to assume that we have a mixture of both A and B in these samples.

9/

Further, we have a unique mutation at 23525 (H655Y), which is also found in e.g., Omicron and Gamma. This appears to be a derived mutation (i.e., not ancestral).

10/

❗️We also have a deletion at 21763-21786 (S:68-75) that leads to the in-frame deletion of "IHVSGTNG".

This deletion is *definitely* derived - because we don't see it in e.g., BANAL-52, RaTG13 - but we *do* see it occuring during the pandemic (e.g., EPI_ISL_434692).

11/

Here's an example of the same deletion observed during the pandemic. Also, note that SRR13441708 has an additional 3bp deletion in this region.

12/ 

Here's what it looks like across the SARSr-CoVs - note how both BANAL-52 and RaTG13 don't have this deletion.

13/ 

❗️Because of this deletion (and to an extent H655Y), we *know* that what we're looking at here is *not* an ancestral virus.

We're likely just looking at clinical samples - or even cultured samples - from early in the pandemic that were sequenced.

14/

Sequenced in Shenzhen btw - probably not where the WIV gets their sequencing done... 🧪

15/

Funny little side note - H655Y has been identified in cats... 🙀

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7685321/

16/

So, again, we know this isn't an ancestral virus - they had just discovered a novel coronavirus in December, 2019 and had many patients - so maybe we should just go with the most likely explanation here?

https://twitter.com/stuartjdneil/status/1491303410821435397?s=20&t=QCB8qpivLQENBsKgHwmZIQ

17/

Could the contaminating virus be one that was cultured (as the preprint suggests)? Sure - they had just discovered a novel coronavirus...

Alternatively, Vero/CHO contaminants may not actually be Vero/CHO. Or, other samples on the same run came from Vero/CHO. 🤷

18/

So why did the original authors delete some of the raw files? Well, probably because they realized there was contamination in their dataset (from the sequencing company) and wanted to sort that out before people might get crazy ideas about antarctic lab leaks. 🤷.

19/

Absolutely nothing unusual about any of this - we observe contaminants all the time and it's up to people analyzing data to realize that and have the expertise to not jump to conclusions based on messy data.

It happens all the time. I wish it didn't, but it does.

20/

And to those of you wondering - no, I did not reject this manuscript on the bioRxiv.

That would just fit the conspiracy theory a little too well, wouldn't it?

Time for rational thinking. 🕵️

21/

Adding this:

https://twitter.com/mvargam/status/1491608829859094533?s=20&t=_-6kuIX-tDK35TfL_nid4w

22/

Correction - Shanghai (at Sangon Biotech), not Shenzhen!

https://twitter.com/K_G_Andersen/status/1491617654540357634?s=20&t=_-6kuIX-tDK35TfL_nid4w

23/

Great comment from @acritschristoph with some additional key points [under "comments"]:

https://www.researchsquare.com/article/rs-1330800/v1

Early SARS-CoV-2 sequencing contamination

🦠 Omicron - on a scale from 1-10, how bad is this going to be? This one's a weirdo, so I'm a 3, a 10, or anything in-between.

A thread below with my take on some of the key questions.

https://outbreak.info/situation-reports/omicron

1/ 

The key questions I'll address in descending order of confidence in currently available evidence:

1. Mutation profile
2. Emergence
3. Immune escape properties
4. Transmission fitness
5. Virulence

2/

1⃣ Gut feeling:

Timing: recent, deep roots (high confidence)
Emergence: animals (very low confidence)
Immune escape: significant (medium-high confidence)
Transmission: increased (low confidence)
Virulence: similar (low confidence)

Details 🧵👇

Before we start, please heed this advice from @EvolveDotZoo because it really is important to understand - there are so many different scenarios that can explain what we're observing. 

So we can discuss "possible" scenarios, but not assign meaningful likelihoods yet.

3/ 

... and read this great article from @kakape on some of the early observations.

https://www.science.org/content/article/patience-crucial-why-we-won-t-know-weeks-how-dangerous-omicron

4/

2⃣ Mutation profile

Omicron has a lot of mutations - more so than previous VOCs. In the spike it has ~40 differences to the original Hu-1 virus (33 AA substitutions, 3 deletions, and 1 insertion). 

You can compare Omicron to other VOCs here:

https://outbreak.info/compare-lineages

5/ 

The recently identified BANAL-52 virus only has ~20 differences to Hu-1 (16 AA substitutions + no furin cleavage site). 

In nucleotide space, however, that switches - 61 for Omicron vs 206 for BANAL-52. Why the difference? 

Selection:

https://twitter.com/sergeilkp/status/1465134289603858435?s=20

6/

Many of the mutations have been seen before in other VOCs, but others have not - and some are 'private' to Omicron (not seen in the larger SARSr-CoV phylogeny). 

Interestingly, many of the mutations are basic R or K with a lot of N&gt;K (pos: 440, 478, 547, 679, 764, 856, 969):

7/ 

So what are these mutations doing? We don't know - and remember, what really matters here is the combination of mutations and not any single mutation on its own. 

Good article here:

https://www.nytimes.com/2021/11/29/health/omicron-covid-mutation-epistasis.html

8/

What is clear though, is that many of these mutations are on the surface of the spike, where, for example, they may be involved in:

- Optimization to human ACE2?
- Optimization to non-human ACE2?
- Alternative/additional receptor?
- Immune evasion?

9/ 

Compare the mutations seen in Omicron vs those in Delta - it's a big difference:

https://twitter.com/COGUK_ME/status/1465455990204252160?s=20

10/

Omicron also has two indels around position 631 that look to be the result of copy-choice recombination, potentially with host, not virus, origin. Recently described in this excellent study:

https://virological.org/t/putative-host-origins-of-rna-insertions-in-sars-cov-2-genomes/761

11/ 

3⃣ Emergence and reservoir

So where did Omicron come from? And when? We don't know... But let's take a look at some of the data and hypotheses. When it comes to age, we can answer two primary questions:

1. When did the lineage brach off?
2. How old is Omicron itself?

12/

It's clear that the lineage leading to Omicron is old - possibly mid-2020, but there's a huge amount of uncertainty in exactly when and where. We also don't know from what basal lineage this branched off and convergent evolution makes this tricky.

https://nextstrain.org/groups/neherlab/ncov/21K.Omicron

13/ 

For how long has Omicron been circulating in humans? We can estimate that based on the diversity in sampled genomes and most estimates land ~mid October (with wide uncertainty), so we believe it's relatively young. 

Good thread from @trvrb here: https://twitter.com/trvrb/status/1464353224417325066?s=20

14/

We believe (a) the lineage leading to Omicron branched off a long time ago, (b) Omicron is young, but (c) is already widespread in parts of Africa. So what led to Omicron? 

Three main hypotheses:

1. Undetected circulation
2. Immunocompromised patient(s)
3. Animal reservoir

15/

I don't believe #1 is likely, leaving evolution in immunocompromised patient(s) or a reverse zoonosis, followed by a new zoonosis (human&gt;animal&gt;human) as the two hypotheses I find most plausible - although I have no confidence in either. 

16/

I slightly favor reverse zoonosis for a few reasons:

1. The lineage is old and undetected circulation in immunocompromised patient(s) for this long seems unlikely
2. SARS-CoV-2 is a generalist virus and we have seen human&gt;animal&gt;human transmission happen in e.g., mink
..

17/

3. Several of the mutations in Omicron have been observed in animals, including rodents:

https://virological.org/t/mutations-arising-in-sars-cov-2-spike-on-sustained-human-to-human-transmission-and-human-to-animal-passage/578/11

18/

Many articles have already concluded that Omicron came from an immunocompromised person ("HIV" is often mentioned), but I disagree with that conclusion. While that's certainly possible, we don't have any data showing that's the case. 

Let's keep all hypotheses open.

19/

4⃣ How did Omicron become widespread so quickly?
We believe Omicron has increased rapidly in frequency for a few reasons:

1. It appears to be displacing Delta in South Africa
..

https://www.youtube.com/watch?v=Vh4XMueP1zQ&t=5s

20/

2. It's been observed in travel associated cases originating from multiple locations, including a single flight with ~10% of passengers being positive for COVID-19 (caveat - not yet clear if they were all Omicron):

https://www.washingtonpost.com/world/2021/11/27/amsterdam-omicron-covid-variant-lockdown/

21/

3. Cases are rising rapidly in places where Omicron seems to be dominating

https://twitter.com/Tuliodna/status/1463911554538160130?s=20

22/

Combine the rapid rise of Omicron with the observation from genetic data that it appears to be relatively young, what best explains that?

1. Increased immune evasion
2. Increased transmission
3. Combination of both

23/

Here's where it gets sketchy - because we really don't have any good data to make any firm conclusions on what/which factor(s) we think contribute the most. 
Remember Oli's advice:

24/ 

The reason why we can't yet distinguish the two, is because a scenario in which Omicron has no immune escape, but is more transmissible can explain what we're observing. The same for a very 'escapy' VOC with the same (or lower) transmission.

https://twitter.com/trvrb/status/1465364300936085506?s=20

25/

This is the difference between estimating R0 (the basic reproductive number) vs Rt (the effective reproductive number, which is Rt = R0*x, where x = fraction of susceptible). 

https://www.healthknowledge.org.uk/public-health-textbook/research-methods/1a-epidemiology/epidemic-theory

26/

Immunity will lower the fraction of susceptibles (x), leading to lower Rt. A more transmissible variant will increase R0, increasing Rt. But, Rt will also increase if a variant is capable of eroding immunity in the population - R0 stays the same, but x is now higher. 

27/

Here lies the problem, because while we can reasonably estimate Rt, we don't know whether an increase is due to R0 being higher (e.g., increased transmission) or x being higher (e.g., increased immune evasion). 

28/

... all of this is also ignoring other factors that influence Rt, including contact patterns, duration of infectiousness, NPI usage, etc.

29/

This means that a more transmissible variant with no difference in immune escape properties *or* a variant with no difference in transmissibility, but with more immune escape properties could explain available evidence.

30/

Good (technical) discussion from @C_Althaus on why it's so hard to distinguish the two: https://twitter.com/C_Althaus/status/1464922681585897478.

31/